{Anthony LAI, Zetta KE}, Researcher
[en] China is a victim, too :-)
アンソニー・ライ、ゼッタ KE
中国はいつも他者を攻撃する攻撃者として認識されているが、逆に「中国が誰かから攻撃を受けているのではないか?」という視点で、どのような攻撃をうけ、どんな理由があるのか?をお見せしよう。
さらに、他の有名な機関から発表されたAPTの調査報告書の内容から、中国からの攻撃を「推測」し、それらの「論理」についてのコメントする。
また、我々はKnownsecからキャプチャされたWeb攻撃データをVXRLで解析を行っており、うまくいけば、より鮮明な絵をお見せすることができると考えている。
もちろん、アジェンダにないオフレコ情報もあるので、みなさんに楽しんでもらえると思う。
China is always taken as an attacker to attack others, let us take a look who is attacking China, what kind of attacks China is suffering from and the possible reason, moreover, we would like to take APT research report published from other famous agency how they "deduce" the attacks from China, commenting on their "logic".
In addition, we have got Knownsec to provide captured and identified Web attack data to VXRL for analysis, hopefully, we could get a much more clearer picture.
Of course, we got a hidden agenda as well.
It would be a fun session and let us enjoy it..
Fighting advanced malware using machine learning (Japanese)FFRI, Inc.
n this paper, behavioral-based detection powered by machine learning is introduced. As the result, detection ratio is dramatically improved by comparison with traditional detection.
Needless to say that malware detection is getting harder today. Everybody knows signature-based detection reaches its limit, so that most anti-virus vendors use heuristic, behavioral and reputation-based detections altogether. About targeted attack, basically attackers use undetectable malware, so that reputation-based detection doesn't work well because it needs other victims beforehand. And it is a fact that detection ratio is not enough though we use heuristic and behavioral-based detections. In our research using the Metascan, average detection ratio of newest malware by most anti-virus scanner is about 30 %( the best is about 60 %).
By the way, heuristic and behavioral-based detections are developed by knowledge and experience of malware analyst. For example, most analysts know that following features are indicator that those programs are malicious.
- A file imports VirtualAlloc, VirtualProtect and LoadLibrary only and has a strange section name
- An entry point that does not fall within declared text or code section
- Creating remote threads into a legitimate process like explore.exe
- After unpacking, calling OpenMutex and CreateMutex to avoid multiple infections
- Register itself to auto start extension points like services and registry
- Creating a .bat file and try to delete own itself through executing the file with cmd.exe
- Setting global hook to capture keystroke using SetWindowsHookEx
Heuristic and behavioral-based detections are developed based on those pre-determined features like above. Analysts are finding those features day by day. But, this kind of work is not appropriate for human. Therefore we classified programs as malware or benign by machine learning through dynamic analysis results. Thereby, detection ratio is dramatically improved and we could recognize that which features are strongly related to malware by numeric score. And then, we could find the features which we’ve never found by this method. Finally, the outlook and challenges of this method will be tackled.
Describing various attack methods on Android/iOS apps. This time I decided to take a quick dive into actual analysis session on the a-bit-hardened InsecureBankV2 with Trueseeing (for #kyusec, Kyushu Security Conference 2018)
AVTokyo 2013.5 - China is a victim, too :-) (English version)Anthony Lai
{Anthony LAI, Zetta KE}, Researcher
[en] China is a victim, too :-)
アンソニー・ライ、ゼッタ KE
中国はいつも他者を攻撃する攻撃者として認識されているが、逆に「中国が誰かから攻撃を受けているのではないか?」という視点で、どのような攻撃をうけ、どんな理由があるのか?をお見せしよう。
さらに、他の有名な機関から発表されたAPTの調査報告書の内容から、中国からの攻撃を「推測」し、それらの「論理」についてのコメントする。
また、我々はKnownsecからキャプチャされたWeb攻撃データをVXRLで解析を行っており、うまくいけば、より鮮明な絵をお見せすることができると考えている。
もちろん、アジェンダにないオフレコ情報もあるので、みなさんに楽しんでもらえると思う。
China is always taken as an attacker to attack others, let us take a look who is attacking China, what kind of attacks China is suffering from and the possible reason, moreover, we would like to take APT research report published from other famous agency how they "deduce" the attacks from China, commenting on their "logic".
In addition, we have got Knownsec to provide captured and identified Web attack data to VXRL for analysis, hopefully, we could get a much more clearer picture.
Of course, we got a hidden agenda as well.
It would be a fun session and let us enjoy it..
Fighting advanced malware using machine learning (Japanese)FFRI, Inc.
n this paper, behavioral-based detection powered by machine learning is introduced. As the result, detection ratio is dramatically improved by comparison with traditional detection.
Needless to say that malware detection is getting harder today. Everybody knows signature-based detection reaches its limit, so that most anti-virus vendors use heuristic, behavioral and reputation-based detections altogether. About targeted attack, basically attackers use undetectable malware, so that reputation-based detection doesn't work well because it needs other victims beforehand. And it is a fact that detection ratio is not enough though we use heuristic and behavioral-based detections. In our research using the Metascan, average detection ratio of newest malware by most anti-virus scanner is about 30 %( the best is about 60 %).
By the way, heuristic and behavioral-based detections are developed by knowledge and experience of malware analyst. For example, most analysts know that following features are indicator that those programs are malicious.
- A file imports VirtualAlloc, VirtualProtect and LoadLibrary only and has a strange section name
- An entry point that does not fall within declared text or code section
- Creating remote threads into a legitimate process like explore.exe
- After unpacking, calling OpenMutex and CreateMutex to avoid multiple infections
- Register itself to auto start extension points like services and registry
- Creating a .bat file and try to delete own itself through executing the file with cmd.exe
- Setting global hook to capture keystroke using SetWindowsHookEx
Heuristic and behavioral-based detections are developed based on those pre-determined features like above. Analysts are finding those features day by day. But, this kind of work is not appropriate for human. Therefore we classified programs as malware or benign by machine learning through dynamic analysis results. Thereby, detection ratio is dramatically improved and we could recognize that which features are strongly related to malware by numeric score. And then, we could find the features which we’ve never found by this method. Finally, the outlook and challenges of this method will be tackled.
Describing various attack methods on Android/iOS apps. This time I decided to take a quick dive into actual analysis session on the a-bit-hardened InsecureBankV2 with Trueseeing (for #kyusec, Kyushu Security Conference 2018)
AVTokyo 2013.5 - China is a victim, too :-) (English version)Anthony Lai
{Anthony LAI, Zetta KE}, Researcher
[en] China is a victim, too :-)
アンソニー・ライ、ゼッタ KE
中国はいつも他者を攻撃する攻撃者として認識されているが、逆に「中国が誰かから攻撃を受けているのではないか?」という視点で、どのような攻撃をうけ、どんな理由があるのか?をお見せしよう。
さらに、他の有名な機関から発表されたAPTの調査報告書の内容から、中国からの攻撃を「推測」し、それらの「論理」についてのコメントする。
また、我々はKnownsecからキャプチャされたWeb攻撃データをVXRLで解析を行っており、うまくいけば、より鮮明な絵をお見せすることができると考えている。
もちろん、アジェンダにないオフレコ情報もあるので、みなさんに楽しんでもらえると思う。
China is always taken as an attacker to attack others, let us take a look who is attacking China, what kind of attacks China is suffering from and the possible reason, moreover, we would like to take APT research report published from other famous agency how they "deduce" the attacks from China, commenting on their "logic".
In addition, we have got Knownsec to provide captured and identified Web attack data to VXRL for analysis, hopefully, we could get a much more clearer picture.
Of course, we got a hidden agenda as well.
It would be a fun session and let us enjoy it..
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。